A Novel Inflammatory Response-Related Gene Signature Improves High-Risk Survival Prediction in Patients With Head and Neck Squamous Cell Carcinoma.

Abstract

Background: Head and neck squamous cell carcinoma (HNSCC) is a highly prevalent and malignant tumor that is difficult to effectively prognosticate outcomes. Recent reports have suggested that inflammation is strongly related to tumor progression, and several biomarkers linked to inflammation have been demonstrated to be useful for making a prognosis. The goal of this research was to explore the relevance between the inflammatory-related genes and HNSCC prognosis. Methods: The clinical information and gene expression data of patients with HNSCC were acquired from publicly available data sources. A multigene prognostic signature model was constructed in The Cancer Genome Atlas and verified in the Gene Expression Omnibus database. According to the risk score calculated for each patient, they were divided into low- and high-risk groups based on the median. The Kaplan–Meier survival curve and receiver operating characteristic curve were applied to determine the prognostic value of the risk model. Further analysis identified the independent prognostic factors, and a prognostic nomogram was built. The relationship between tumor immune infiltration status and risk scores was investigated using Spearman correlation analysis. Finally, to confirm the expression of genes in HNSCC, quantitative real-time polymerase chain reaction (qRT-PCR) was performed. Results: A prognostic model consisting of 14 inflammatory-related genes was constructed. The samples with a high risk had an apparently shorter overall survival than those with a low risk. Independent prognostic analysis found that risk scores were a separate prognostic factor in HNSCC patients. Immune infiltration analysis suggested that the abundance of B cells, CD8 T cells, M2 macrophages, myeloid dendritic cells, and monocytes in the low-risk group was higher, while that of M0, M1 macrophages, and resting NK cells was obviously higher in the high-risk group. The risk scores were related to chemotherapeutic sensitivity and the expression of several immune checkpoint genes. Moreover, CCL22 and IL10 were significantly higher in HNSCC tissues, as determined by qRT-PCR. Conclusion: Taken together, we constructed a novel inflammatory response–related gene signature, which may be used to estimate outcomes for patients with HNSCC and may be developed into a powerful tool for forecasting the efficacy of immunotherapeutic and chemotherapeutic drugs for HNSCC.